The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

Abstract

Prioritization of pathways to perform asphalt pavement operations has always been one of the most important concerns for municipalities, for which, currently there is no specific planning and pattern. In the present study, using (Unmanned Aerial Vehicle) UAV images, a land cover map of the case study was prepared. For this purpose, the accuracy of various object-based classification methods including the Bayes method, the Support Vector Machine (SVM), the K nearest neighbor (KNN), the Decision tree (DT), and the Random tree (RT) was investigated. Findings of the study showed that by increasing heterogeneity in the composition of the studied phenomenon in the image, different classification algorithms offer results different from each other. The obtained results of the accuracy evaluation of classification methods indicate that the SVM method with 80% kappa coefficient and 89% overall accuracy had the best performance compared to other methods. As a result, built-up land covers, bare land, vegetation cover, and paved roads were separated using this method. Then, the exact boundary of pathways was prepared using Google Earth images, and then, using the land-use map prepared from the case study, the roads were divided into two categories: paved and unpaved. To determine the prioritization of unpaved roads for applying asphalt, the proportion of built-up lands (BUL) to bare (non-built-up) lands (BL) was used in each path. Based on the obtained results, 1% of the roads in the case study was placed on a very high level of asphalt, and then 9%, 3%, 49%, 38%, were placed on a high priority to low priority, respectively.